Scholarlink

Purpose <p>This study investigates the potential of preoperative MEG functional connectivity networks to predict the efficacy of vagus nerve stimulation (VNS) in patients with drug-resistant epilepsy (DRE).</p> Methods <p>A total of 18 DRE patients and 18 healthy controls were enrolled. Resting-state MEG data were collected preoperatively, and brain network connectivity was assessed across seven frequency bands (δ, θ, α, β, γ, ripple, and fast ripple) using corrected amplitude envelope correlation (AEC-c). Network-based statistics (NBS) were employed to identify differences in connectivity patterns.</p> Results <p>Compared to healthy controls, DRE patients, particularly non-responders (NR-VNS), exhibited widespread abnormal functional connectivity, including significant increases in low-frequency bands and mixed alterations in mid-to-high frequency bands. Responders (R-VNS) showed marked normalization of brain connectivity, with reductions in differences from controls, especially within alpha and beta bands. These connectivity patterns were significantly associated with treatment outcomes, indicating their potential as predictive biomarkers.</p> Conclusions <p>Preoperative brain network patterns derived from multi-frequency MEG, particularly in alpha and beta bands, hold promise for predicting VNS treatment response in DRE patients. The “health status” of the brain’s network prior to implantation appears to be a crucial factor influencing therapeutic efficacy.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Preoperative MEG reveals differential brain network characteristics in drug-resistant epilepsy patients based on vagus nerve stimulation response

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月12日

查看全文
  • Lingling Yang,
  • Minghao Li,
  • Hongxing Liu,
  • Ying fan Wang,
  • Jing Lu,
  • Yuejun Li,
  • Fangqing Chen,
  • Haitao Zhu,
  • Haiyan Ma,
  • Yiqing Yang,
  • Qiqi Chen,
  • Lu Yang,
  • Xuefeng Qu,
  • Rui Zhang,
  • Xiaoshan Wang

摘要

Purpose

This study investigates the potential of preoperative MEG functional connectivity networks to predict the efficacy of vagus nerve stimulation (VNS) in patients with drug-resistant epilepsy (DRE).

Methods

A total of 18 DRE patients and 18 healthy controls were enrolled. Resting-state MEG data were collected preoperatively, and brain network connectivity was assessed across seven frequency bands (δ, θ, α, β, γ, ripple, and fast ripple) using corrected amplitude envelope correlation (AEC-c). Network-based statistics (NBS) were employed to identify differences in connectivity patterns.

Results

Compared to healthy controls, DRE patients, particularly non-responders (NR-VNS), exhibited widespread abnormal functional connectivity, including significant increases in low-frequency bands and mixed alterations in mid-to-high frequency bands. Responders (R-VNS) showed marked normalization of brain connectivity, with reductions in differences from controls, especially within alpha and beta bands. These connectivity patterns were significantly associated with treatment outcomes, indicating their potential as predictive biomarkers.

Conclusions

Preoperative brain network patterns derived from multi-frequency MEG, particularly in alpha and beta bands, hold promise for predicting VNS treatment response in DRE patients. The “health status” of the brain’s network prior to implantation appears to be a crucial factor influencing therapeutic efficacy.